Metastatic lung adenocarcinoma with BRCA2 mutation and longstanding disease control on olaparib, developing triple negative breast adenocarcinoma with additional BRCA2 reversion mutation: a case report

BACKGROUND

The BRCA2 gene is a well-known tumor suppressor gene implicated in breast and ovarian cancers. BRCA1/2 mutations can be sensitive to poly ADP-ribose polymerase (PARP) inhibitors such as olaparib. However, some of these patients develop resistance to this treatment and an essential factor contributing to acquired insensitivity is the occurrence of reversion mutations in the BRCA1/2 genes.

CASE PRESENTATION

We report the case of a 65-year-old Brazilian female patient who had previously been diagnosed with metastatic lung carcinoma carrying a BRCA2 mutation that had extended to the central nervous system. Following disease progression, olaparib was administered, resulting in a stabilizing effect on her condition for ~ 30 months. During a routine follow-up, a new triple-negative breast tumor was found. Genetic testing revealed the presence of two distinct BRCA2 gene mutations in the breast tumor. The original mutation (p.Val220Ilefs4) led to a frameshift, culminating in the production of a truncated and non-functional BRCA2 protein; the second mutation, K437fs22, rectified the reading frame of exon 11. Consequently, Rad51 could properly bind to BRCA2-an essential protein crucial for DNA repair. This restoration resulted in a functional BRCA2 protein, effectively elucidating the clinical resistance observed in the new breast tumor in this case.

CONCLUSIONS

This case report highlights the clinical significance of comprehensive next-generation sequencing analyses for lung adenocarcinomas, both at diagnosis and upon progression. Such analyses enable informed decisions regarding targeted therapies and facilitate a deeper comprehension of resistance mechanisms.

Using a convolutional neural network for classification of squamous and non-squamous non-small cell lung cancer based on diagnostic histopathology HES images

Histological stratification in metastatic non-small cell lung cancer (NSCLC) is essential to properly guide therapy. Morphological evaluation remains the basis for subtyping and is completed by additional immunohistochemistry labelling to confirm the diagnosis, which delays molecular analysis and utilises precious sample. Therefore, we tested the capacity of convolutional neural networks (CNNs) to classify NSCLC based on pathologic HES diagnostic biopsies. The model was estimated with a learning cohort of 132 NSCLC patients and validated on an external validation cohort of 65 NSCLC patients. Based on image patches, a CNN using InceptionV3 architecture was trained and optimized to classify NSCLC between squamous and non-squamous subtypes. Accuracies of 0.99, 0.87, 0.85, 0.85 was reached in the training, validation and test sets and in the external validation cohort. At the patient level, the CNN model showed a capacity to predict the tumour histology with accuracy of 0.73 and 0.78 in the learning and external validation cohorts respectively. Selecting tumour area using virtual tissue micro-array improved prediction, with accuracy of 0.82 in the external validation cohort. This study underlines the capacity of CNN to predict NSCLC subtype with good accuracy and to be applied to small pathologic samples without annotation.

Expression of selected miRNA, RARβ and FHIT genes in BALf of squamous cell lung cancer (squamous-cell carcinoma, SCC) patients: a pilot study

Two suppressor genes which often undergo epigenetic silencing during the early stages of lung carcinogenesis are those encoding retinoic acid receptor-β (RARβ) and Fhit protein (FHIT). RARβ expression is regulated by miRNA-34a and miRNA-141, and FHIT expression by miRNA-143 and miRNA-217. The aim of the study was to assess how selected miRNAs regulate the expression of their targeted genes in bronchoalveolar lavage fluid (BALf), obtained from patients with SCC of the lung. It also examines the relationship between the genetic findings and the clinical and pathomorphological features of the tumor. A total of 50 BALf samples were taken: 25 from patients with SCC and 25 from healthy donors. The expression (RQ) of the selected genes was analyzed by qPCR, as well as the miRNA level, with a particular emphasis on the relationship between the expression of the genes themselves and their corresponding miRNAs; in addition, the expression of the genes and miRNAs were compared with the pathomorphological features of the tumor and the clinical features of patients. Analysis of the RQ values showed downregulation of RARß, FHIT and miRNA-34a and increased expression of miRNA-141, miRNA-143 and miRNA-217 in all BALf samples (P > 0.05). No correlation was found between the expression of the selected genes and corresponding miRNAs, history of smoking, cancer stage, age and sex of the patients. The presence of the selected genes and miRNAs in BALf material does not seem to have diagnostic potential in patients with SCC; however, the results should be verified on a larger group of patients.

Update on emerging biomarkers in lung cancer

There has been considerable progress made in identifying oncogenic driver mutations in advanced lung cancer. The recognition that lung cancer is actually an umbrella classification that is comprised of multiple molecular subgroups has had a profound impact on how medical oncologists make treatment decisions. These mutations are clinically important as available targeted therapies can achieve significant responses and prolonged disease control. This review will summarize the current guidelines for biomarker testing and available therapeutic agents.

Companion and Complementary Diagnostics-Focus on PD-L1 Expression Assays for PD-1/PD-L1 Checkpoint Inhibitors in Non-Small Cell Lung Cancer

Over the last couple of decades, molecular diagnostics have played an increasing role in drug development. Especially within oncology, more and more drugs are being developed together with a predictive biomarker assay using the drug-diagnostic codevelopment model. Not only do these assays support the development process but also the use of the drugs after regulatory approval as an important treatment decision tool. When these predictive biomarker assays are linked to a specific drug, they are called companion diagnostics. Furthermore, these assays are also considered an important element in the realization of precision medicine. Today, 21 different drugs have obtained US FDA approval together with a companion diagnostic assay, and the requirement for testing is part of their regulatory labeling. More than half of these drugs are for treatment of non-small cell lung cancer (NSCLC). With the approval of the different programmed cell death 1 (PD-1)/programmed cell death ligand 1 (PD-L1) immune checkpoint inhibitors, for the treatment of advanced stage NSCLC, a new class of predictive biomarker assays-complementary diagnostics-has emerged. Until now, 3 immune checkpoint inhibitors have obtained regulatory approval for treatment of NSCLC, and they all have a biomarker assay linked to their use. However, only for pembrolizumab, the PD-L1 immunohistochemical (IHC) 22C3 pharmDx assay has status as a companion diagnostic. For nivolumab and atezolizumab, the assays PD-L1 IHC 22C3 pharmDx and Ventana PD-L1 (SP142) have status as complementary diagnostics, which means that there are no requirements for testing included in the labeling for these drugs. Here, the authors discuss the clinical performance of the different IHC PD-L1 expression assays including the selection of the clinical cutoff values.

Updates in Lung Cancer Cytopathology

Lung cancer diagnosis and ancillary testing are increasingly relying on cytology and small biopsy specimens obtained via minimally invasive means. Paired with traditional immunohistochemical characterization of tumors, biomarker testing and comprehensive genomic profiling are becoming essential steps in the workup of lung cancer to identify targetable alterations and guide optimal therapy selection. Recent advances in immune checkpoint inhibitor therapy have led to an increasingly complex and unresolved landscape for tumor PD-L1 testing. The prevalence and importance of lung cancer cytology specimens are growing, with more required by the cytopathologist in directing the care of patients with lung cancer.

Automated chromogenic multiplexed immunohistochemistry assay for diagnosis and predictive biomarker testing in non-small cell lung cancer

OBJECTIVES

The current challenge in the management of non-small cell lung cancer (NSCLC) in pathology laboratories is to combine immunohistochemistry (IHC) and molecular approaches on increasingly smaller biopsies and the need to reserve a fair amount of tumor material for molecular analyses with increasingly larger panels. The latest lung cancer classification, especially in the setting of poorly differentiated tumors, requires an IHC workup to allow for accurate diagnosis and also to preserve as much tissue as possible for molecular testing. Thus, it is recommended to reduce use of the term NSCLC not otherwise specified as much as possible and classify tumors according to their specific histologic subtype. This implies limiting the number of tissue slides despite the existence of specific and sensitive biomarkers (ALK, ROS1, BRAF V600E, PD-L1) and the obligation to distinguish lung adenocarcinoma (TTF-1 positive) from squamous cell carcinoma (p40 positive).

MATERIALS AND METHODS

Samples from 18 patients with NSCLC, previously characterized for histologic and genomic/immune features, were included. Two multiplexed IHC assays were developed, for diagnosis and immunophenotyping including TTF1, p40, PD-L1, and pan-Keratin antibodies, and for molecular profiling panel including ALK, ROS1 and BRAF V600E antibodies.

RESULTS

We developed two sensitive multiplexed IHC assays to comprehensively characterize major NSCLC histotypes and FDA-cleared predictive biomarkers, without antigenicity loss, steric interference or increased cross-reactivity. The assays rely on standard antigen retrieval and automated staining protocols, limiting the need for validation strategies.

CONCLUSION

Our multiplexed IHC approach provides a unique sample-sparing tool to characterize limited tissue samples in lung oncology and making it an alternative method in the clinical setting for therapeutic decision making of advanced NSCLC, provided that validation in a larger population is performed.

An Update on Predictive Biomarkers for Treatment Selection in Non-Small Cell Lung Cancer

It is now widely established that management of lung cancer is much more complex and cannot be centered on the binary classification of small-cell versus non-small cell lung cancer (NSCLC). Lung cancer is now recognized as a highly heterogeneous disease that develops from genetic mutations and gene expression patterns, which initiate uncontrolled cellular growth, proliferation and progression, as well as immune evasion. Accurate biomarker assessment to determine the mutational status of driver mutations such as EGFR, ALK and ROS1, which can be targeted by specific tyrosine kinase inhibitors, is now essential for treatment decision making in advanced stage NSCLC and has shifted the treatment paradigm of NSCLC to more individualized therapy. Rapid advancements in immunotherapeutic approaches to NSCLC treatment have been paralleled by development of a range of potential predictive biomarkers that can enrich for patient response, including PD-L1 expression and tumor mutational burden. Here, we review the key biomarkers that help predict response to treatment options in NSCLC patients.